Hugging Face's logo
John Smith's picture
In a Training Loop πŸ”„
527 2311 28269

John Smith PRO

John6666
BigPancake01's profile picture Sakiji's profile picture MoonknightLive's profile picture
Β·

AI & ML interests

None yet

Recent Activity

reacted to AdinaY's post with πŸš€ 7 minutes ago
AgentCPM-ExploreπŸ”₯ on device agent foundation model released by OpenBMB https://huggingface.co/openbmb/AgentCPM-Explore ✨ 4B - Apache2.0 ✨ Supports 100+ multi-turn environment interactions with search + verification ✨ Full training/inference stack is openly shared as well
reacted to AbstractPhil's post with πŸš€ 7 minutes ago
The Long: this is a proof of concept; ensemble compilation vmap prototype is functional and can be used to increase throughput for wider batches on FFN, MLP, LLM, or other models than just ensembles. This system traces your model and creates stages of functional activation. Based on the stage it will combine or remove combinations of stages meant to assign your layers to batched functional calls meant to put pressure on your GPU with less loops with directly curated cudagraph compliance where applicable. Identical weights yield identical results at the cost of hardware and vram. TLDR: This is an ensemble optimization adapted to standard models. This will yield high-capacity speed improvements through increased throughput for inference and training alike using carefully traced staged vmap structures. https://github.com/AbstractEyes/pytorch-parallel-compiler The early list of layers isn't fully represented yet, so this is a preliminary look into the potentials of this structure when fully fleshed out. MLP (N=100, batch=32, CUDA): ``` Eager: 2-3x speedup Compiled: 35-40x speedup ``` ResBlock (N=20, batch=8, CUDA): ``` Eager: ~5x speedup Compiled: ~10x speedup ``` This is early testing and so far the yields indicate that WIDENING your model with adjacent shared batched vmaps for uniformly staged models will yield considerably higher output for inference at the cost of additional hardware utilization. This is akin to lining up all your systems and uniformly passing the necessary implications through a shared frozen representation gate. Training for this is not tested nor supported yet, use at your own risk.
reacted to sergiopaniego's post with πŸš€ 8 minutes ago
Recursive Language Models (RLM) is a new interface for LLMs with cool ideas by Alex Zhang! ⚠️ LLMs struggle with long prompts β†’ attention overload & lost info πŸ”„ RLMs inspect, split & call themselves on chunks, then aggregate results βœ… Handles millions of tokens, reduces noise, improves reasoning πŸ’‘ System prompt guides recursion 🎯 RLM trajectories can be used for RL training or distillation (OpenEnv+TRL!!) We're adding it to OpenEnv (with Kashif Rasul): https://github.com/meta-pytorch/OpenEnv/pull/282 More resources: > Paper: https://huggingface.co/papers/2512.24601 > Paper blog: https://alexzhang13.github.io/blog/2025/rlm/ > RLM repo: https://github.com/alexzhang13/rlm
View all activity

Organizations

Glide's profile picture open/ acc's profile picture Solving Real World Problems's profile picture FashionStash Group meeting's profile picture No More Copyright's profile picture XORTRON - Criminal Computing's profile picture